import torch
from torch.utils.data import DataLoader
from model import SCM, Generator, Discriminator, DRRecommender
from data_loader import load_data
from config import Config

def train():
    config = Config()
    data = load_data(config)
    
    # Initialize models
    scm = SCM(data['n_users'], data['n_items'], config)
    generator = Generator(config)
    discriminator = Discriminator(config)
    model = DRRecommender(scm, generator, discriminator, config)
    
    # Optimizers
    opt_main = torch.optim.Adam(model.parameters(), lr=config.lr)
    opt_gan = torch.optim.Adam(generator.parameters(), lr=config.gan_lr)
    
    # Training loop
    for epoch in range(config.epochs):
        for batch in DataLoader(data['train'], batch_size=config.batch_size):
            # Train main model with causal adjustment
            real_pred = model.scm(batch['users'], batch['items'])
            
            # Generate counterfactual data
            z = torch.randn(len(batch), config.noise_dim)
            fake_data = model.generator(z, real_pred)
            
            # Compute dual robust loss
            loss = model.compute_loss(real_pred, fake_data, data['popularity'])
            
            # Update parameters
            opt_main.zero_grad()
            loss.backward()
            opt_main.step()
            
            # Train GAN components
            # (Implementation of adversarial training would go here)
            
        # Validation and logging
        if epoch % 10 == 0:
            evaluate(model, data['test'])

def evaluate(model, test_data):
    # Implement evaluation metrics (NDCG, Recall, EFI, etc.)
    pass

if __name__ == "__main__":
    train()
